GenAI Platforms Compared¶

In one line: Choose where to call a frontier or open-weights model in production (first-party API, cloud platform or inference host) by models, tool use, caching, privacy and price. Last reviewed: 2026-10-21

Where to call a frontier or open-weights LLM in production. This is the API-and-model-routing layer, not the agent or app framework layer (see Agent frameworks comparison for that).

For underlying concepts: LLM basics, Tool use, Prompt caching.

Platform capabilities change slowly; model names change every few months. Model generations named below (Claude 3.x/4, GPT-5, Gemini 2) were current when this was written in 2025 and newer ones exist as of October 2026. Use the model family explainers for what is current.

Decision matrix¶

"Native tool use" means structured tool/function calling at the API level. "Prompt caching" means a real cache mechanism (not just retries).

Anthropic API¶

Direct access to Claude (Opus / Sonnet / Haiku families). Anthropic's first-party endpoint.

• Models: Claude 3.5/3.7/4 Opus, Sonnet, Haiku tiers; vision; computer use beta on supported models.
• Tool use: native, excellent with multi-step agentic workflows. Strong support for parallel tool calls.
• MCP: Anthropic created the Model Context Protocol; first-class client support.
• Prompt caching: ephemeral cache breakpoints (~5min TTL, ~25% write premium, ~10% read cost) and 1-hour extended cache.
• Streaming: SSE-based streaming; events include thinking, tool calls, stop reasons.
• Regions: US, EU. Workspaces for org-level isolation.
• Quotas: tier-based; enterprise has higher limits.
• No HIPAA BAA on first-party API directly - use Bedrock or Vertex for regulated workloads.

Pick Anthropic API when: you're building a Claude-first app and want the latest model features fastest (multi-step tool use, computer use, MCP).

📖 Anthropic API docs - models, messages API, tool use 📖 Claude pricing - per-token, per-tier

OpenAI API¶

The original frontier-model API. GPT-5 family, o-series reasoning models, embeddings, fine-tuning.

• Models: GPT family, o-series reasoning models, image and audio modalities (4o), embeddings, Whisper, DALL-E.
• Tool use: native function calling; structured outputs with strict JSON schemas.
• Prompt caching: automatic on prompts >1024 tokens, cached read at ~50% off; no manual breakpoints.
• Realtime API: low-latency voice and audio agents.
• Assistants API and Responses API: server-side agent loops with persistent threads.
• Regions: global; data residency available on enterprise tiers.
• Privacy: enterprise customers get data-not-used-for-training and zero-data-retention options.

Pick OpenAI API when: you need GPT-class models, o-series reasoning, voice / realtime, or built-in agent state via Assistants/Responses.

📖 OpenAI API docs - chat completions, function calling, streaming 📖 OpenAI pricing - per-model pricing

AWS Bedrock¶

Single API to many models including Anthropic Claude, Meta Llama, Mistral, AI21, Cohere, Stability, Amazon's own (Titan, Nova), and others.

• Models: Claude (full Anthropic family on Bedrock with slight version lag), Llama ¾, Mistral, Cohere Command, AI21 Jamba, Amazon Titan/Nova.
• Tool use: native; per-model availability varies.
• Prompt caching: yes for supported models (Claude family).
• Knowledge Bases: managed RAG (see vector DB comparison).
• Agents for Bedrock: managed agentic loop with tool/action groups.
• Guardrails: configurable input/output filters, PII detection.
• Privacy: VPC endpoints, PrivateLink, customer-managed KMS, HIPAA BAA, no data used for training.
• Regions: most major AWS regions; model availability varies.

Pick Bedrock when: you're on AWS, want VPC private endpoints, multi-model flexibility behind one API, or HIPAA/regulated workloads. Especially good when you want managed RAG (Knowledge Bases) or managed agents.

📖 Bedrock documentation - models, agents, knowledge bases 📖 Bedrock pricing - per-token + provisioned throughput

Azure OpenAI Service¶

OpenAI's GPT family hosted by Microsoft on Azure infrastructure.

• Models: GPT family, embeddings, Whisper. Slight lag behind OpenAI direct on newest models.
• Tool use: native.
• Prompt caching: same as OpenAI semantics.
• Privacy: VNet integration, private endpoints, customer-managed keys, HIPAA BAA.
• Regions: many Azure regions; model availability varies by region.
• Microsoft 365 integration: Copilot APIs, Graph data connectors, Azure AI Search.
• Content safety: built-in content filters configurable per deployment.

Pick Azure OpenAI Service when: you're on Azure, integrating with Microsoft 365, or need GPT in regulated regions with VNet isolation.

📖 Azure OpenAI documentation - service overview 📖 Azure OpenAI pricing - per-token

Google Agent Platform¶

Google's GenAI platform. Access to Gemini (1.5, 2.x), Anthropic Claude (via partnership), open models (Llama, Mistral, Gemma), and your own custom-deployed models.

• Models: Gemini family (text, image, video, code), Claude on Vertex, Llama ¾, Mistral, Mixtral, Gemma, plus Model Garden for many more.
• Tool use: native function calling.
• Context caching: explicit cache creation with TTL; useful for large stable contexts (e.g. 1M-token Gemini windows).
• Grounding: built-in grounding with Google Search.
• Privacy: VPC-SC, CMEK, HIPAA BAA, data not used for training.
• Regions: global; model availability varies.
• BigQuery / Vertex Vector Search proximity: easy retrieval against GCP data.

Pick Agent Platform when: you're on GCP, want Gemini's very long context windows, or want to deploy/serve custom models alongside frontier APIs through one platform.

📖 Agent Platform generative AI docs - Gemini, Claude on Vertex, Model Garden 📖 Agent Platform pricing - per-token, per-modality

Together AI¶

Specialist host for open-weights and fine-tuned models. Per-token pricing comparable to or lower than hosted-frontier APIs.

• Models: hundreds of OSS models (Llama ¾, Mixtral, Mistral, Qwen, DeepSeek, etc.).
• Tool use: yes for supported models.
• Fine-tuning: serverless LoRA fine-tunes.
• Inference: optimized vLLM-style infra.
• Pricing: per-token, generally lower than hosted frontier.

Pick Together AI when: you want OSS models in production at competitive prices without running inference servers yourself.

📖 Together AI documentation - models, inference, fine-tuning

Fireworks AI¶

Similar shape to Together: managed serving of OSS models with strong performance focus.

• Models: Llama, Mixtral, others; some proprietary Fireworks-tuned variants.
• Tool use: native function calling on supported models.
• Performance: known for low latency, particularly on smaller and quantized models.
• Fine-tuning: LoRA and full fine-tunes.

Pick Fireworks when: Together-like need but with stronger latency or specific model tunings.

📖 Fireworks AI documentation - models, function calling, tuning

Groq¶

Custom LPU (Language Processing Unit) hardware specialized for very-low-latency LLM inference.

• Models: curated OSS models (Llama family, Mixtral, Qwen, etc.).
• Latency: typically the fastest tokens/sec in the industry, often 5-10x faster than GPU-based hosting.
• Tool use: native on supported models.
• Limits: smaller catalog, no full-frontier models like Claude or GPT.

Pick Groq when: latency is the primary constraint - voice agents, interactive UIs, anything where time-to-first-token under 200ms matters.

📖 Groq documentation - models, API

Pick by scenario¶

flowchart TD
  A{Need a specific frontier model?}
  A -->|Claude| B{Where?}
  B -->|Anthropic-direct, latest features| ANT[Anthropic API]
  B -->|AWS / VPC / HIPAA| BR[Bedrock]
  B -->|GCP| VX[Vertex AI]

  A -->|GPT family| C{Where?}
  C -->|OpenAI direct, latest| OAI[OpenAI API]
  C -->|Azure / VNet / Microsoft 365| AOA[Azure OpenAI]

  A -->|Gemini| VX2[Vertex AI]

  A -->|OSS models| D{Constraint?}
  D -->|Cost| TG[Together AI]
  D -->|Latency| GQ[Groq]
  D -->|AWS-native| BR2[Bedrock]
  D -->|GCP-native| VX3[Vertex AI Model Garden]
  D -->|Self-host| SH[Self-host with vLLM<br/>see inference servers]

Cost intuition (per 1M input tokens, mid-2026 spot)¶

Numbers move; this is for relative ordering only. Several sample models below have since been superseded, so treat the table as a snapshot of mid-2025 list prices and confirm with vendor calculators. The Inference economics explainer carries sourced price lists as of September 2026.

Next steps¶

• Concepts: LLM basics, Tool use, Prompt caching, Inference servers
• Topic: LLMs and GenAI
• Related comparisons: Vector databases, Agent frameworks, LLM observability
• Certs: Anthropic Architect Foundations, AWS AI Practitioner, Azure AI-102, Databricks GenAI Engineer

In the research library:

• AI labs landscape - who builds the models
• Claude - model family explainer
• GPT - model family explainer
• Gemini - model family explainer
• Open vs closed weights - the API vs self-host trade-off
• Inference economics - current list prices and how to read them
• Which model for which job - decision guide

Sources¶

• Anthropic, pricing: https://www.anthropic.com/pricing
• OpenAI, API pricing: https://openai.com/api/pricing/
• Google, Gemini API pricing: https://ai.google.dev/gemini-api/docs/pricing
• Amazon Bedrock, supported models: https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html